Abstract
Identifying the appropriate parameters of a turbulence model for a class of flow usually requires extensive experimentation and numerical simulations. Therefore even a modest improvement of the turbulence model can significantly reduce the overall cost of a three-dimensional, time-dependent simulation. In this paper we demonstrate a novel method to find the optimal parameters in the Reynolds-averaged Navier–Stokes (RANS) turbulence model using high-fidelity direct numerical simulation (DNS) data. A physics informed neural network (PINN) that is embedded with the turbulent transport equations is studied, physical loss functions are proposed to explicitly impose information of the transport equations to neural networks. This approach solves an inverse problem by treating the five parameters in turbulence model as random variables, with the turbulent kinetic energy and dissipation rate as known quantities from DNS simulation. The objective is to optimize the five parameters in turbulence closures using the PINN leveraging limited data available from costly high-fidelity DNS data. We validated this method on two test cases of flow over bump. The recommended values were found to be \(C_{\epsilon 1}\) = 1.302, \(C_{\epsilon 2}\) = 1.862, \(C_{\mu }\) = 0.09, \(\sigma _K\) = 0.75, \(\sigma _{\epsilon }\) = 0.273; the mean absolute error of the velocity profile between RANS and DNS decreased by 22% when using these neural network inferred parameters.
Keywords
- Turbulence modeling
- Neural network
- Physics embedded machine learning
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Acknowledgments
This work utilizes resources supported by the National Science Foundation’s Major Research Instrumentation program, grant #1725729, as well as the University of Illinois at Urbana-Champaign.
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Luo, S., Vellakal, M., Koric, S., Kindratenko, V., Cui, J. (2020). Parameter Identification of RANS Turbulence Model Using Physics-Embedded Neural Network. In: Jagode, H., Anzt, H., Juckeland, G., Ltaief, H. (eds) High Performance Computing. ISC High Performance 2020. Lecture Notes in Computer Science(), vol 12321. Springer, Cham. https://doi.org/10.1007/978-3-030-59851-8_9
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DOI: https://doi.org/10.1007/978-3-030-59851-8_9
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